基于Voronoi图和蚁群优化算法的无人作战飞机航路规划  

Path Planning of Uninhabited Combat Air Vehicle Based on Voronoi Diagram and Ant Colony Optimization Algorithm

作　　者：刘森琪[1] 段海滨[1,2] 余亚翔[1] 

机构地区：[1]北京航空航天大学自动化科学与电气工程学院,北京100083 [2]苏州大学江苏省计算机信息处理技术重点实验室,江苏苏州215006

出　　处：《系统仿真学报》2008年第21期5936-5939,共4页Journal of System Simulation

基　　金：国家自然科学基金(60604009);航空科学基金资助项目(2006ZC51039);北京科技新星计划资助项目(2007A017);苏州大学江苏省计算机信息处理技术重点实验室开放课题基金(KJS0821)

摘　　要：无人作战飞机(UCVA)航路规划是一类复杂优化问题。在众多航路规划算法中,Voronoi图是一种根据战场多威胁源分布情况获取可行航路的图形算法,而蚁群优化(ACO)算法是受到蚂蚁觅食行为启发而形成的一种启发式仿生算法。根据已知威胁源生成Voronoi加权图,其中每条Voronoi边的总代价可以由威胁代价和燃油代价计算得出;然后给出了在Voronoi图条件下,用于航路规划的改进ACO算法模型和具体实现方法;最后,将Voronoi图与ACO算法相结合,并针对某UCAV多种空战态势下的航路规划问题进行了系列仿真实验。实验结果验证了所提方法在解决UCAV航路规划问题时的可行性和有效性。Path planning of Uninhabited Combat Air Vehicle （UCAV） is a complicated optimum problem, and a common graphical technique for optimal path planning against multiple threat sources is to make use of the Voronoi diagram. Ant Colony Optimization （ACO） algorithm is a heuristic bionic algorithm for the approximate solution of combinatorial optimization problems, which has been inspired by the foraging behavior of real ant colonies. Firstly, the weighted Voronoi diagram was created according to the certain threat sources, and the total cost of each edge couM be calculated according to the threats cost and the fuel cost. Then, the improved ACO mathematical model for UCAV path planning was proposed. Finally, a hybrid Voronoi diagram and ACO approach to UCA Vpath planning was put forward Series simulation results demonstrate the proposed hybrid method is feasible and effective in UCAV path planning under various combat field environments.

关 键 词：无人作战飞机(UCAV) 航路规划 VORONOI图 蚁群优化(ACO) 信息素 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]